Membrane curvature regulates the spatial distribution of bulky glycoproteins

The glycocalyx is a shell of heavily glycosylated proteins and lipids distributed on the cell surface of nearly all cell types. Recently, it has been found that bulky transmembrane glycoproteins such as MUC1 can modulate membrane shape by inducing membrane protrusions. In this work, we examine the reciprocal relationship of how membrane shape affects MUC1’s spatial distribution on the cell membrane and its biological significance. By employing nanopatterned surfaces and membrane-sculpting proteins to manipulate membrane curvature, we show that MUC1 avoids positively-curved membranes (membrane invaginations) and accumulates on negatively-curved membranes (membrane protrusions). MUC1’s curvature sensitivity is dependent on the length and the extent of glycosylation of its ectodomain, with large and highly glycosylated forms preferentially staying out of positive curvature. Interestingly, MUC1’s avoidance of positive membrane curvature enables it to escape from endocytosis and being removed from the cell membrane. These findings also suggest that the truncation of MUC1’s ectodomain, often observed in breast and ovarian cancers, may enhance its endocytosis and potentiate its intracellular accumulation and signaling.

All nanoX are 350 nm in width and 10 µm in spacing. NanoX Inner angle (θ) increment: 15o . 6 would validate the mechanism. However, the data show that the colocalization of MUC1 and I-BAR is not at all affected by the length of the ectodomain. Similarly, the triple mutation did not affect the colocalization of MUC1-Ts with IRSp53. To the same degree that the experiments with the F-BAR validate the mechanism, the lack of response in the I-BAR experiments refutes it. This is a significant problem that is neither acknowledged nor addressed, let alone resolved. 4. Claim: "In sharp contrast, MUC1_42TR-GFP prefers the flat sidewalls of thin nanobars but avoids the two ends with positive curvatures (Fig. 7E). " I do not think that the data I see in 7E or in 7G support this claim. This appears to be in contradiction with the analysis shown in Fig. 7I. The authors should comment on this apparent discrepancy. 5. Claim: "Through these quantitative measurements, we determined the curvature threshold, or the cutoff value, to be ~ 1/1200 nm for MUC1_42TR-GFP's avoidance of positive curvatures. The threshold diameter at 1200 nm is much larger than the diameter of clathrin-coated pits (50-200 nm)." My understanding of the way entropic forces-based curvature sensing works is based on the data from the Stachowiak group on IDPs. There the do not see, nor expect to see, thresholds but rather 1) monotonic dependencies that 2) get steeper as curvatures increase. Both of these features appear to contradict the findings of Fig. 7 suggesting a different mechanism. The authors should comment on this apparent discrepancy.
6. Claim: "For this purpose, we designed and fabricated the gradient "NanoX" arrays which are able to induce positive curvatures at the arm ends and negative curvatures at the inner faces ( Fig. 2A). " The authors should provide support for the claim that membrane conforms to the X shapes. 7. Claim: "For dense nanopillar arrays, Fig. 2I, the inter-pillar spacing was small enough to induce the formation of membrane protrusions with negative curvatures in the inter-pillar spaces. " The authors should provide data to support this claim. 8. Claim: "Fluorescence recovery after photobleaching (FRAP) measurement shows that supported lipid bilayers formed on SiO2 nanostructures exhibited similar fluidity as on flat areas (Suppl. Fig. S12)". S12 does not compare FRAP curves on the two areas and thus does not support this claim.
General comments: Work with IDPs would suggest that surface density, i.e expression levels, strongly influence the coupling to curvature. Have the authors quantified the expression levels of the different mutants and if so what where they, and where they the same.
It would be very helpful to provide info on the number of different cells and numbers of pilar used on the averages, in addition to the number of independent repeats that should be ≥ 3 in all data sets (especially when SEM is used to reduce s.d.).
Reviewer #2 (Remarks to the Author): The manuscript titled: "Membrane curvature regulates the spatial distribution of bulky glycoproteins" by Lu et al examines the biophysical behavior of MUC1, a bulky glycoprotein implicated in a range of important biological functions, in curved membranes. The authors used a powerful combination of nanoarray technologies and molecular biology to enforce curvatures on cell membranes with tunable geometries to evaluate how perturbations in membrane morphology affects MUC1 distribution in the membranes.
This work builds on a recent discovery that bulky mucins have the capacity to enforce curvature in membranes and this work shows that this relationship is reciprocal and that membrane curvature may likewise lead to reorganization of the mucinous glycocalyx, which can have biological consequences, such as increased resistance of mucins to enter endosomes leading to their prolonged cell surface residence.
The key takeaways from this study are the findings that bulky mucins avoid membranes with negative curvatures and that this propensity increases with the bulk of the mucin ectodomain (as defined by its length and level of glycosylation) and membrane density (shown in supported lipid bilayers).
The methods are well designed and the data are clearly presented. All appropriate controls are included and the manuscript is very well written and easy to follow.
The paper provides important new insights into the mechanobiology of the mucinous glycocalyx and I recommend is suitable for publication as is.
I only had two minor comments for the authors to consider. 1) In certain instances, I found it difficult to visually confirm the changes in Muc1 localization on the nanopillar nanobar arrays using the fluorescence micrographs in Figures 1 and 2. For instance in Fig 1 K an M, I was having a hard time observing the avoidance of the positively curved ends of the nanobar by MUC1-42TR. The distribution of the protein appears quite similar to that of CAAX. Similarly, the distributions of MUC1 in Fig 2 D and E are quite difficult to assess visually (the additional SI heatmaps help). I understand that the authors applied a custom MATLAB program to quantify changes in fluorescence. Perhaps a description of how that process works in the SI would be helpful.
2) My second comment relates to the role of the cytosolic domain of Muc1 in the membrane localization process. The authors comment on its role in endocytosis and signaling. I wonder to what extent the interactions of the cytosolic domain with the cytoskeleton may counteract the avoidance of positive curvatures by MUCs. Or can the avoidance of such membrane regions drive the reorganization of the cytoskeleton?
Reviewer #3 (Remarks to the Author): The manuscript "Membrane curvature regulates the spatial distribution of bulky glycoproteins" by Lu et al. describes a series of experiments conducted to look out for the influence of membrane curvature on the glycoprotein MUC1.
The manuscript is overall well written and organized. The authors proceed in a clear structure for the different experiments to consecutively follow logicalyl a path to evidence the proposed hypothesis on how the membrane curvature impacts the distribution of MUC1. The work in the experiments is substantial and makes the idea of physical crowding / space requirements being the driving force for the observation that MUC1 avoids positively curved membrane areas.
The only criticism I have is about the presentation and discussion of the entanglement of whether gycoproteins cause membrane curvature (as shown by the works referenced in the second abstract of the introduction) or are guided by membrane curvature, or more likely what the extend of both effects is. I think the best evidence for the "sensing not sculpturing" is given by the author also adding the in vitro experiments, as there a very simple system without any other components and proteins that could play a role in addition to the MUC1 is presented. So in this context, I was feeling a bit confused when after the the second paragraph (with the references to work showing glycoproteins can cause membrane curvature), the next paragraph first discussed generally about the role of membrane curvature for cell physiology and only then comes back to the contrast "curvature sculpture agains sensing" again. So I would suggest to move the general introduction to the role of membrane curvature (the passage "Cell membranes are dynamic [...] cell invasion") to another place, maybe directly after the introduction on MUC1 or even as the overall start of the whole introduction, to have the contrasting section ("Although it is now established [...]") directly following on the introduction of glycoproteins sculpturing curvature. This would in my opinion even more clealy present the main point and novelty of the authors work.
One question to the fluorescence images in Figure 1c. Looking at the a-MUC1 / CellMask channels, it looks like there is a kind of "window" in the cell with overall reduced fluorescence intensity, as only in this region the raised CellMask signal on/near the pillars is clearly visible (while the rest looks more homogeniuosly red). Additionally , the a-MUC1 signal is raised in these areas to a point where the pillars become much more visible (e.g. in the area right to the dash marked box). Have the authors some idea for what this effect is?
And (also Figure 1c), for further clarity, I suggest that the authors include a full set (a-AP2, CellMask, Birght field) of images also for the seperate experiment in the fourth panel (a-AP2) -can be done in the supporting information of course to not clutter the Figure 1 even more.
Having raised these points, I want to conclude with that I really like the paper and would recommend publication with only minor revision for the case that the authors would like to revise their manuscript a bit in regard to the above points.
Reviewer #4 (Remarks to the Author): The authors report a novel and rigorous study on the dependence of glycoprotein distribution on cell membrane curvature, which was induced by either nanostructures or membrane-sculpturing proteins. This is a strong study supported by a large amount of work. In particular, the nanostructures included a variety of geometric shapes and curvature values. Some concerns and suggestions are discussed below.
1. Page 4: "we observed that in areas where some nanopillars were accidentally scratched off (areas circled in dashed yellow lines in Fig. 1D" The nanopillars that were scratched off are still visible in the bright field image, suggesting some remaining (e.g., a pillar broken in the middle) or opposite (e.g., a pit formed in the portions where a pillar is scratched off) topography, which might affect the membrane curvature and MUC1 distribution. Is it possible to image a cell on the boundary of the patterned pillars, which covers both pillars and original planar surface (instead of the scratched area)? This would exclude any unknown effect of scratched pillars. For example, in Fig. 1D why does the circled area have a much higher overall MUC1 intensity (and lower Cell Mask intensity) than the pillar area, considering there are large planar empty spaces among the pillars? This seems inconsistent with Fig. 2I, where the MUC1 intensity on pillar area and planar area are comparable, just with different distributions due to the pillar-induced membrane curvatures.

Fig. 5C
The averaged normalized intensity is uniform on the nanoX top planar surface (except the dark center area), which is higher than the intensity on nanoX outlines (contours).
This seems inconsistent with Fig. S12 A and Fig. 2E, where the fluorescence intensity is concentrated on the nanoX outlines, the X-shape center and arms are all dark.
Minor concerns 1. Page 3: "Although it is now established that overexpression of bulky glycocalyx proteins can induce membrane protrusions, the reciprocal relationship ..." This study revealed that the glycoprotein prefers negative membrane curvature. Do these findings provide a new angle to explain the mechanism of glycoproteins inducing protrusions? Could you comment on this mutually beneficial relationship between glycoprotein and protrusions? How are they a pair of mutual causes and effects? 2. Page 3: "Previous studies by us and others show that when cells are cultured on substrates with vertical nanopillars, the plasma membrane wraps around nanopillars to create local membrane curvatures (Fig. 1B)." Fig. 1B only shows a schematic that the membrane closely wraps the nanostructures. Are there experimental verifications of this basic assumption? The membrane might either closely or loosely wrap the nanostructures, how is this geometry-dependent? This would also provide the rationale of the geometric design (e.g., aspect ratio and pillar height).
For example, Page 6: "For dense nanopillar arrays, the inter-pillar spacing was small enough to induce the formation of membrane protrusions with negative curvatures in the inter-pillar spaces." How is the inter-pillar spacing optimized here to ensure that the pillars closely imprint membrane protrusions? It is also possible that the membrane could be almost planar on dense pillar tips if the gap space is too small for the membrane to penetrate. BTW, is it possible to provide a zoom-in overlay in Fig. S7 to compare with that in Fig. 2I? 3. Page 5: "MUC1_42TR and MUC1_21TR show spatial avoidance of the nanobar ends." From Fig 1M, it is difficult to see the avoidance on nanobar ends. Would it be helpful if providing the nanobar outline as a guide for the eye? With precise positions indicated by the outline, it might show that the intensity is relatively weaker on the two ends than over the whole bar region (just a suggestion). 4. Fig. 1E, I, Fig. 2A show schematics of marked outlines for the ratio calculation. Could you provide the actual regions of interest? For example, in Fig. 1E, is the ROI a circle-shape region, or a ring-shape covering only the pillar outline and excluding the top surface?
For all above nanostructures, the authors considered the membrane curvature induced in the lateral X-Y plane, not the vertical Y-Z plane where a positive curvature also exists. But for Fig. 2H, the dense pillarinduced protrusions were actually in the vertical plane, not lateral X-Y plane. Does the vertical curvature also play a role (contribute to the intensity on nanostructure top surfaces) in the previous pillar/nanobar/nanoX cases where mainly X-Y plane curvatures were considered (intensity mostly concentrated in the outlines)? If there is such a crosstalk between vertical and lateral membrane curvatures, could it be minimized by a good focusing in the confocal microscopy (vertical resolution ~0.5um)? i.e., If it is ideally focused on a cross-section of the nanostructure, would the imaging show only the lateral X-Y plane signals, without the effect from vertical curvature? Could you comment on this?

RESPONSES TO REVIEWERS' COMMENTS
We thank the reviewers for their constructive and critical comments that have been VERY helpful in improving the quality of our manuscript. In response to reviewers' comments, we performed many new experiments, added new analyses, and substantially revised the main text and the figures. Below are the point-by-point responses to reviewers' comments.
To all reviewers: before addressing individual reviewer's comments, we would like to provide clarification on the data analysis. Reviewers' comments made us realize that we didn't clearly explain the quantification method and how the conclusions were drawn. Here, we describe step-by-step from the raw images to the quantified values, with detailed statistics of the number of nanobars, the number of cells, and the number of experiments. Figure R1 shows data processing steps for a fixed U2OS cell co-expressing MUC1_42TR-GFP and mCherry-CAAX.
Step 1: load three-channel (GFP, mCherry, and bright field) images taken from the same field.
Step 2: Manually click on the center of three nanobars (red arrows) in the mCherry-CAAX channel. Since the distance between nanobars is fixed, the software automatically locates all the nanobars in a rectangular array (yellow circles). Next, the software automatically propagates the nanopillar locations to all three color channels (GFP, mCherry, and bright field).
Step 3: By intensity thresholding in the GFP channel, the software removes nanobars that are located outside the cell of interest. In some cases, a nanobar outside the cell needs to be manually removed by clicking anywhere inside its yellow circle.
Step 4: Based on the nanobar locations, the software automatically creates an averaged nanobar image from all the nanobars inside a cell. The cell in the example image interacts with 100 nanobars. In general, each cell contacts~30-150 nanobars. An average nanobar image is created for each color channel for the selected cell.
Step 5: Four ROIs, two located at the ends of the nanobar and two located at the side walls of the nanobar, are created on the membrane mCherry channel of the averaged nanobar image. The same ROI locations are re-created on the GFP channel. The same ROIs are used for all cells.
Step 6: From the ROIs, the nanobar end/side ratios are independently calculated for mCherry and GFP channels. Then, the ratio for the MUC1_42TR-GFP channel (green) is divided by the ratio for the mCherry-CAAX channel (red). This step normalizes the protein ratio to the membrane ratio, which helps to distinguish whether the protein truly has a curvature preference vs. whether there is a higher protein signal due to more membranes at curved locations.

Figure R1:
Step-by-step illustration of nanobar data processing and quantification. Three channels-mCherry-CAAX, MUC1-GFP, and bright field, from the same location are needed. This step-by-step quantification process is now provided in Suppl. Figure S2. Figure R2 shows the example image and the averaged images of nanobars over many cells with detailed statistics shown in Table R1. As in the example images of MUC1_42TR and alpha-AP2, some of the nanobars show clear protein accumulation (responsive) while others do not (non-responsive). In order to avoid any user-bias, we included all nanobars inside cells when calculating the average image. Including the non-responsive nanobars likely increases the overall background of the average image and makes the curvature effect less visible to the eye. Quantification of the end/side ratios over many cells shows that the differences between MUC1_42TR/MUC1_21TR and CAAX are statistically significant (p<0.0001). In the revised manuscript, we also include data points for individual cells, with each cell data representing the average of 50-100 nanobars.   Figure R2D. The substrate that was used to measure AP2 and F-actin has many nanobars scratched off. There were fewer nanobars per cell.

Reviewer #1 (Remarks to the Author):
The MS by Lu et al. describes the ability of MUC1 (one of the major components of the mammalian glycocalyx) to sense the curvature of the plasma membrane and prefer/avoid areas of respectively -ve/+ve curvature. MUC1's curvature preference is suggested to reduce its endocytic uptake. Overall the MS is very well written, and referenced, and the figures are very well presented. Mechanistically MUC1-curvature sensing appears to bear strong similarities with IDPs as investigated by Stachowiak et al. They have demonstrated in two NatComm papers first that IDPs help induce curvature and then they can sense it. It appears that the curvature inducing properties of MUC1 have already been documented. One could claim that curvature sensing by MUC1 is rather predictable, and not that exciting at least from a biophysical perspective. However, it would appear that there is sufficient biological interest in demonstrating curvature sensing by MUC1 and by extension glycoproteins, as e.g. in the case of endocytosis. However, as I explain below in detail, I have major concerns on whether many of the claims in this MS are supported by the data, thus I am unfortunately not able to recommend publication.
We strongly agree with the reviewer that MUC1's curvature sensing is likely the same IDPs and dominated by entropic forces. We added discussions of IDPs in the introduction of the similarity. "Recent studies show that, due to conformational entropy, intrinsically disordered regions (IDRs) are able to sense membrane curvatures when artificially tethered to the membrane and can amplify the curvature sensitivity of BAR domains (Zeno et al. 2018;Snead et al.). Therefore, MUC1 may sense membrane curvatures via a similar mechanism." 1. Claim: "On the same nanobars, MUC1_42TR-GFP appears to distribute more on the flat side walls (Fig. 1K)". Upon close inspection of Fig. 1K and especially of the averages in Fig. 1M, I see uniform distribution for 21TR and 42TR. Which appears to be in contradiction with the analysis shown in Fig. 1N. The authors should comment on this apparent discrepancy.
We thank the reviewer for commenting about the "apparent discrepancy", which makes us realize that we didn't clearly explain the quantification method and how the conclusions were drawn. Reviewer 2 has similar comments about the quantification. We apologize for the poor explanation/presentation in the previous version of the manuscript. As shown in Figures R1 and  R2, we describe step-by-step from the raw images to the quantified values, with detailed statistics of the number of nanobars and the number of cells. In Figure R3 below, we show enlarged average images of all the proteins. We can see visible differences between 21TR/42TR and the CAAX membrane control. The quantified end/side ratios shown in Fig. R2 are normalized against CAAX. In the revised manuscript, we also include data points for individual cells in Fig. R2, with each cell data representing the average of 50-100 nanobars. The difference between 21/42TR and CAAX is statistically significant (p<0.0001).
To clarify, we modified the sentence to "On the same nanobars, MUC1_42TR-GFP appears to distribute more on the flat side walls as compared to the CAAX membrane control.". Figure R3: Enlarged average images of different proteins on nanobars. CAAX shows a slightly stronger signal at nanobar ends because of increased membrane area at the curved location.
2. Claim: "In contrast, on the same nanoXs, MUC1_42TR-GFP accumulated more at the inner faces with negative curvatures.". After close inspection of Fig. 2C-D, and of the averages in 2E/S5, I am not able to confirm the claim about 42TR. Actually, in S5 intensities appear higher on the edges. Which appears to be in contradiction with the analysis shown in Fig. 2F. The authors should comment on this apparent discrepancy.
We thank the reviewer for pointing this out. We should have been more precise in our claims. The claim for MUC1_42TR-GFP is relative to the CAAX membrane control. We modified the claims to "In contrast, on the same nanoXs, MUC1_42TR-GFP accumulated more at the inner faces with negative curvatures as compared to the CAAX membrane control.".
The quantification of nanoXs is similar to the quantification of nanobars for step 1-4. After obtaining the average nanoX images for MUC1-GFP and mCherry-CAAX channels for each cell, 16 ROIs are selected on the membrane channel as shown in Fig. R4. The same ROI locations are applied to both channels and for all cells. Ratios are separately quantified for the MUC1-GFP channel and the mCherry-CAAX channel. Then, the ratios for proteins (GFP channel) are normalized against the ratios for membranes (mCherry channel). The normalization against the membrane is important because a protein may appear to accumulate at high positive curvature locations, such as mCherry-CAAX showing higher intensities at the ends of nanoXs ( Figure R4), but the effects are due to membrane wrapping and 2D projection instead of a real curvature effect. If a membrane protein such as MUC1_0TR-GFP does not have a curvature preference, the ratios will be similar to CAAX, and thus the normalized ratios will be close to 1. On the other hand, the ratios for 42TR are statistically different from CAAX, and the trend is opposite to F-actin. We updated the quantification to include data points for individual cells (Fig. R4). Figure R4: The data processing for nanoX and the quantified protein distributions on nanoX.
3. In the experiments with I-, F-bars, according to the way the authors have interpreted the results from fig. 1 and 2, one would expect that truncation would produce an antisymmetric response (up/down concentration) to the +/-curvatures. Such an antisymmetric response would validate the mechanism. However, the data show that the colocalization of MUC1 and I-BAR is not at all affected by the length of the ectodomain. Similarly, the triple mutation did not affect the colocalization of MUC1-Ts with IRSp53. To the same degree that the experiments with the F-BAR validate the mechanism, the lack of response in the I-BAR experiments refutes it. This is a significant problem that is neither acknowledged nor addressed, let alone resolved. This is an important point that was not clearly articulated in the previous version of the manuscript. Here, we clarify how we quantified the colocalization. Our colocalization measurements are automatically processed by image registration of two color channels after background subtraction, followed by pixel-by-pixel correlation using the built-in function of ImageJ, which calculates a Pearson's correlation coefficient (PCC) for each pair of images. Unlike the nanobar end/side ratios that depend on the intensity, the colocalization is binary yes/no for each pixel. For example, MUC1_42TR-GFP strongly prefers negative curvature of filopodia. MUC1_0TR-GFP does not have a curvature preference and thus distributes evenly on filopodia and flat membranes. Therefore, in IRSp53 co-transfected cells, we expect that MUC1_42TR-GFP and MUC1_0TR-GFP show similar colocalizations with IRSp53 that is mostly on filopodia. On the other hand, MUC1_42TR-GFP is largely absent on FBP17-labeled membrane invaginations, while MUC1_0TR-GFP distributes evenly on membrane invaginations and flat membranes. Therefore, in FBP17 co-transfected cells, MUC1_42TR-GFP and MUC1_0TR-GFP will show different colocalizations with FBP17.
The colocalization between MUC1 and IRSp53 is much less than 100%. This is because, unlike CAAX that shows very little intracellular retention, there is always a fraction of MUC1 proteins trapped in ER or other intracellular organelles, similar to previous reports (Pan et al. 2019;Hisatsune et al. 2009). Nevertheless, there is a significant difference between MUC1 correlations to IRSp53 vs. FBP17 -the Pearson correlation coefficient (PCC) between MUC1_42TR-GFP and IRSp53 is significantly higher than that between MUC1_42TR-GFP and FBP17.
To further address this comment, we quantified a different colocalization parameter, thresholded Mander's overlap coefficient (MOC), which is also calculated in ImageJ similar to Pearson's correlation coefficient (PCC). MOC has been shown to provide similar but sometimes complementary information than PCC(Dunn et al. 2011). Using the same set of data used for PCC calculation, the new MOC quantifications show the same trend as the PCC (Fig. R5). PCC is more widely used in literature, thus we keep using PCC quantifications in Fig. 3E, 3F, 4E, 4F and S12C.  4. Claim: "In sharp contrast, MUC1_42TR-GFP prefers the flat sidewalls of thin nanobars but avoids the two ends with positive curvatures (Fig. 7E). " I do not think that the data I see in 7E or in 7G support this claim. This appears to be in contradiction with the analysis shown in Fig. 7I. The authors should comment on this apparent discrepancy.
We should have been more precise in our claims. We are referring to the protein distribution normalized to the membrane distribution. From Fig. 7E, MUC1_42TR-GFP accumulates to the flat sidewalls of some thin nanobars, while other nanobars do not show clear MUC1_42TR-GFP signals. We averaged all nanobars of the same size to obtain the averaged image of MUC1_42TR shown in Fig. 7G. The averaged image of CAAX from the co-transfection experiment was also obtained in the same way (Fig. S17). The quantified end-to-side ratios of MUC1_42TR shown in Fig. 7I are normalized against the CAAX ratios. To clarify, we revised the sentence to "For thin nanobars, in contrast to the membrane marker mCherry-CAAX that shows slightly higher intensity at the ends of nanobars, MUC1_42TR-GFP shows a lower intensity at the ends of nanobars than at the flat sidewalls (Fig. 7E, white  arrowheads).". 5. Claim: "Through these quantitative measurements, we determined the curvature threshold, or the cutoff value, to be~1/1200 nm for MUC1_42TR-GFP's avoidance of positive curvatures. The threshold diameter at 1200 nm is much larger than the diameter of clathrin-coated pits (50-200 nm)." My understanding of the way entropic forces-based curvature sensing works is based on the data from the group on IDPs. There they do not see, nor expect to see, thresholds but rather 1) monotonic dependencies that 2) get steeper as curvatures increase. Both of these features appear to contradict the findings of Fig. 7 suggesting a different mechanism. The authors should comment on this apparent discrepancy.
We agree with the reviewer that the mechanism should be similar to IDPs via an entropic exclusion. We also agree with the reviewer that "monotonic decrease" is a more accurate description than "threshold". Even our previous studies that report "threshold" for curvature-sensing proteins are actually monotonic decays instead of on-off switches. We revise the sentence to "Through these quantitative measurements, we determined the curvature range with an upper diameter boundary~1200 nm for MUC1_42TR-GFP's avoidance of positive curvatures. The upper diameter boundary at 1200 nm is much larger than the diameter of clathrin-coated pits (50-200 nm)." 6. Claim: "For this purpose, we designed and fabricated the gradient "NanoX" arrays which are able to induce positive curvatures at the arm ends and negative curvatures at the inner faces ( Fig. 2A). " The authors should provide support for the claim that the membrane conforms to the X shapes.
The plasma membrane adheres tightly to the ends of nanoX, but likely does not conform to inner surfaces of nanoXs. From previous electron microscopy studies (Santoro et al. 2017), we learn that the cell membrane attaches tightly to protruding surfaces, but does not adhere to invaginating surfaces. This can be seen from the membrane marker mCherry-CAAX signals in Figure 2E, which shows a much higher intensity at the four ends than the inner sides. Although negative curvatures will still be induced at the inner faces of nanoXs, the curvature value can not be precisely defined. This is also why there is no apparent difference between the two complementary inner surfaces (e.g. 30 o vs. 150 o ) in nanoXs (Fig. 2F-G). To avoid confusion, we added the following text to the manuscript "The plasma membrane adheres to the ends of nanoX, but likely not tightly to inner surfaces of nanoXs. This can be seen from mCherry-CAAX signals (Fig. 2E and Suppl. Fig. S6B and S7), which shows a much higher intensity at the four ends than the inner sides. Although negative curvatures are induced at the inner faces of nanoXs, the curvature value is not defined by the angle, which explains why there is no apparent difference between the two complementary inner surfaces in nanoXs (Fig. 2F-G).". 7. Claim: "For dense nanopillar arrays, Fig. 2I, the inter-pillar spacing was small enough to induce the formation of membrane protrusions with negative curvatures in the inter-pillar spaces. " The authors should provide data to support this claim.
We have previously used TEM to investigate how cells interface with nanopillar arrays with the dimension used in this study (both sparse and dense arrays). The image shown in Figure R6  . We believe that these protrusions can grow into the inter-pillar space, which is why MUC1 is located at inter-pillar spaces as shown in Fig. 2I. We also provided the zoom-in overlay in Fig. S9, which confirms the same observation.  Fig. S12)". S12 does not compare FRAP curves on the two areas and thus does not support this claim.
We thank the reviewer for pointing out the missing control. We now added the result of the FRAP experiments performed on flat surfaces vs. on nanoXs ( Figure R7). The FRAP experiments show that the lipid fluidities on both nanoXs and flat surfaces are comparable. This result is consistent with our earlier work and with reports from other research groups (Zhao et al. 2017;Su et al. 2020). The Figure S12 in the previous version becomes Figure S14 in the new version. General comments: The prevailing natural phenotype in the spatial distribution of MUC1, as seen in all cell figures in this MS, appears to be puncta. The authors should comment on this and if possible link them to curvature. Work with IDPs would suggest that surface density, i.e expression levels, strongly influence the coupling to curvature. Have the authors quantified the expression levels of the different mutants and if so what where they are, and where they are the same. It would be very helpful to provide info on the number of different cells and numbers of pillars used on the averages, in addition to the number of independent repeats that should be ≥ 3 in all data sets (especially when SEM is used to reduce s.d.).
We thank the reviewer for requesting details of statistical analysis. We should have provided this in the initial submission. In the revised manuscript, we added a table, Table 1 To address the reviewer's comment about expression levels for different mutants, we performed new flow cytometry experiments. All MUC1 mutants are labeled with a GFP tag. Flow cytometry of 7-different MUC1 mutants show that all MUC1ΔCT-GFP constructs are expressed at a similar level on cell surfaces (Fig. R8, added as Suppl. Figure   For MUC1 immunostaining on HeLa cells, we employed Anti-MUC1/episialin Antibody, clone 214D4 (Sigma-Aldrich) to probe the extracellular domain of the endogenous MUC1. The reviewer is correct that the prevailing natural phenotype of MUC1, probed by 214D4, appears to be puncta. A recent study shows the natural MUC1, as probed by 214D4, is almost exclusively distributed on the membrane protrusions with negative curvatures (Li et al. 2021). We added new texts in the main text to suggest a possible link between natural MUC1's puncta distribution, its relation to curvature, and the implication in MUC1 endocytosis.

Reviewer #2 (Remarks to the Author):
The manuscript titled: "Membrane curvature regulates the spatial distribution of bulky glycoproteins" by Lu et al examines the biophysical behavior of MUC1, a bulky glycoprotein implicated in a range of important biological functions, in curved membranes. The authors used a powerful combination of nanoarray technologies and molecular biology to enforce curvatures on cell membranes with tunable geometries to evaluate how perturbations in membrane morphology affects MUC1 distribution in the membranes.
This work builds on a recent discovery that bulky mucins have the capacity to enforce curvature in membranes and this work shows that this relationship is reciprocal and that membrane curvature may likewise lead to reorganization of the mucinous glycocalyx, which can have biological consequences, such as increased resistance of mucins to enter endosomes leading to their prolonged cell surface residence.
The key takeaways from this study are the findings that bulky mucins avoid membranes with negative curvatures and that this propensity increases with the bulk of the mucin ectodomain (as defined by its length and level of glycosylation) and membrane density (shown in supported lipid bilayers).
The methods are well designed and the data are clearly presented. All appropriate controls are included and the manuscript is very well written and easy to follow.
The paper provides important new insights into the mechanobiology of the mucinous glycocalyx and I recommend it is suitable for publication as is.
We thank the reviewer for the positive comments about this work! I only had two minor comments for the authors to consider. 1. In certain instances, I found it difficult to visually confirm the changes in Muc1 localization on the nanopillar nanobar arrays using the fluorescence micrographs in Figures 1 and 2. For instance in Fig 1 K an M, I was having a hard time observing the avoidance of the positively curved ends of the nanobar by MUC1-42TR. The distribution of the protein appears quite similar to that of CAAX. Similarly, the distributions of MUC1 in Fig 2 D and E are quite difficult to assess visually (the additional SI heatmaps help). I understand that the authors applied a custom MATLAB program to quantify changes in fluorescence. Perhaps a description of how that process works in the SI would be helpful.
This comment makes us realize that we didn't clearly explain the quantification method. Reviewer 1 has similar comments about the quantification. We apologize for the poor explanation/presentation in the previous version of the manuscript. As shown in Figures R1 and  R2, we describe step-by-step from the raw images to the quantified values, with detailed statistics of the number of nanobars and the number of cells. In Figure R9, we show enlarged average images of all the proteins. We can see visible differences between 21TR/42TR and the CAAX membrane control. The quantified end/side ratios are normalized against CAAX. We also include data points for individual cells in Fig. R9, with each MUC1 cell data representing the average of 40-100 nanobars. The difference between 42TR and CAAX is statistically significant (p<0.0001). The distributions of MUC1 in Fig 2D and 2E are similarly processed. We added the step-by-step quantification process in Suppl. Figure S1. To address this comment, we carried out a new set of co-transfection experiments using full-length MUC1_42TR-GFP (with intracellular domain) and IRSp53 or FBP17. The new data shows that full-length MUC1_42TR-GFP preferentially accumulates at IRSp53-induced negative membrane curvatures, but avoids FBP17-induced positive ones. In terms of curvature preference, full-length MUC1_42TR-GFP behaves similarly to MUC1ΔCT_42TR-GFP ( Figure  R10, added as Suppl. Figure S12). Phalloidin co-staining shows that actin filaments are present in IRSp53-induced membrane protrusions that also show strong MUC1 accumulation, but not in FBP17-induced invaginations. These new data are added to Suppl. Fig S12.   Figure R10: Full-length MUC1 with intracellular domain, colocalizing with IRSp53 but not with FBP17.

Reviewer #3 (Remarks to the Author):
The only criticism I have is about the presentation and discussion of the entanglement of whether glycoproteins cause membrane curvature (as shown by the works referenced in the second abstract of the introduction) or are guided by membrane curvature, or more likely what the extend of both effects is. I think the best evidence for the "sensing not sculpturing" is given by the author also adding the in vitro experiments, as there a very simple system without any other components and proteins that could play a role in addition to the MUC1 is presented. So in this context, I was feeling a bit confused when after the the second paragraph (with the references to work showing glycoproteins can cause membrane curvature), the next paragraph first discussed generally about the role of membrane curvature for cell physiology and only then comes back to the contrast "curvature sculpture against sensing" again. So I would suggest to move the general introduction to the role of membrane curvature (the passage "Cell membranes are dynamic [...] cell invasion") to another place, maybe directly after the introduction on MUC1 or even as the overall start of the whole introduction, to have the contrasting section ("Although it is now established [...]") directly following on the introduction of glycoproteins sculpturing curvature. This would in my opinion even more clearly present the main point and novelty of the authors' work.
We thank the reviewer for pointing out the entanglement of the logic flow in the text. We totally agree with the reviewer's suggestion and made substantial changes in the text to avoid the back-and-forth discussion of the curvature sensing and curvature generating. The text reads much better than the previous version.
1. One question to the fluorescence images in Figure 1c. Looking at the a-MUC1 / CellMask channels, it looks like there is a kind of "window" in the cell with overall reduced fluorescence intensity, as only in this region the raised CellMask signal on/near the pillars is clearly visible (while the rest looks more homogeneously red). Additionally , the a-MUC1 signal is raised in these areas to a point where the pillars become much more visible (e.g. in the area right to the dash marked box). Have the authors had some idea of what this effect is?
The "window" is the location where the nucleus is located. The nucleus location often has a lower fluorescence background because there is less background fluorescence from the cytosol or from the intracellular membrane. As a result, signals from the bottom cell membrane appear to be more pronounced. Because MUC1 is a transmembrane protein, it also shows a weak signal at nanopillar locations where the membrane areas are larger than surroundings, albeit at a much lower level than protrusions at the peripheral of the cell.
The reviewer asked why "only in this region the raised CellMask signal on/near the pillars is clearly visible (while the rest looks more homogeneously red)". This is related to the properties of the "CellMask" dye. This dye is supposed to label the plasma membrane. However, unlike the protein marker CAAX that exclusively labels the plasma membrane, it is known that the CellMask dye can penetrate the plasma membrane to label intracellular membranes, depending on the dye incubation time. The red image in Figure 1C (the previous version) shows a high background due to intracellular membrane labeling. The high background masks the nanopillar effect to make the regions look homogeneous. As a comparison, the plasma membrane labeled by CAAX (Fig. S5A and S5B) shows membrane wrapping on all nanopillars. To clarify, we added in the manuscript "We note that CellMask can also stain intracellular membranes at a low intensity, thus giving an overall higher background than CAAX.". In the revised version of the manuscript, we have substantially rearranged Figure 1. The Figure 1C in the previous version becomes Figure 1K in the new version.
2. And (also Figure 1c), for further clarity, I suggest that the authors include a full set (a-AP2, CellMask, Bright field) of images also for the separate experiment in the fourth panel (a-AP2)can be done in the supporting information of course to not clutter the Figure 1 even more.
We have now added a full set (a-AP2, CellMask, Bright field) of images in Figure R11, also added as Suppl. Figure S4A. Having raised these points, I want to conclude that I really like the paper and would recommend publication with only minor revision for the case that the authors would like to revise their manuscript a bit in regard to the above points.
We thank the reviewer for the positive feedback about this work!

Reviewer #4 (Remarks to the Author):
The authors report a novel and rigorous study on the dependence of glycoprotein distribution on cell membrane curvature, which was induced by either nanostructures or membrane-sculpturing proteins. This is a strong study supported by a large amount of work. In particular, the nanostructures included a variety of geometric shapes and curvature values. Some concerns and suggestions are discussed below.
1. Page 4: "we observed that in areas where some nanopillars were accidentally scratched off (areas circled in dashed yellow lines in Fig. 1D" The nanopillars that were scratched off are still visible in the bright field image, suggesting some remaining (e.g., a pillar broken in the middle) or opposite (e.g., a pit formed in the portions where a pillar is scratched off) topography, which might affect the membrane curvature and MUC1 distribution. Is it possible to image a cell on the boundary of the patterned pillars, which covers both pillars and original planar surface (instead of the scratched area)? This would exclude any unknown effect of scratched pillars. For example, in Fig. 1D why does the circled area have a much higher overall MUC1 intensity (and lower Cell Mask intensity) than the pillar area, considering there are large planar empty spaces among the pillars? This seems inconsistent with Fig. 2I, where the MUC1 intensity on pillar area and planar area are comparable, just with different distributions due to the pillar-induced membrane curvatures.
To address this concern, we carried out new experiments and imaged cells that grew at the boundary of nanopillar/flat areas. Similar to the nanopillar scratched-off results, we found that MUC1 prefers flat areas rather than nanopillar areas. The old images are replaced by the new images in Figure 1L in the new version. We would like to explain the seemingly inconsistency between Figure 1D and Figure 2I. Hela cells express a high level of MUC1 and these cells also have extensive filopodia protrusions (unlike U2OS cells). Previous studies show that MUC1 preferentially distributes on these filopodia protrusions with negative curvature. Figure 1D (in the old version) (200-nm diameter, 2.5-μm spacing) and Figure 2I (500 nm diameter, 1 um spacing) differ in their nanopillar array dimensions. From Figure 1D, it appears that filopodia (negative curvatures) preferentially occur on the portion of the cell that is in contact with the flat areas. We speculate that it is because nanopillars create positively membrane curvatures, which makes it unfavorable for filopodia formation that requires the generation of negative membrane curvatures. For Figure  2I, the nanopillars are bigger and densely packed. The cell membrane is likely not wrapping around the nanopillars (See TEM images shown in Figure R6) such that the inter-pillar space can effectively induce or accommodate membrane protrusions. More discussions about the large and dense nanopillars are included later when addressing another comment.
We added in the text "Interestingly, when cells were at the nanopillar/flat boundary, MUC1 appeared to prefer the flat region rather than the nanopillar region (Fig. 1D). Previous studies show that MUC1 in Hela cells largely localizes on filopodia protrusions. We speculate that nanopillars create positively membrane curvatures, which makes it unfavorable for filopodia formation that requires the generation of negative membrane curvatures.".
2. Fig. 5C: The averaged normalized intensity is uniform on the nanoX top planar surface (except the dark center area), which is higher than the intensity on nanoX outlines (contours). This seems inconsistent with Fig. S12 A and Fig. 2E, where the fluorescence intensity is concentrated on the nanoX outlines, the X-shape center and arms are all dark.
For Fig. 5C and Fig. S12A (previous version of the manuscript), both images show supported lipid bilayers on the nanoX structures. When the focal plane is near the bottom of the nanoX such as Fig. S12A, the X structure shows a dark center. When the focal plane is near the middle height of nanoX such as Fig. S12B, S12C and S12D, the X structure does not show a dark center because the top surface of nanoX is slightly inside the imaging volume. We try to focus near the middle height of nanoX, but sometimes the focal plane deviates slightly such as that in Fig. S11A. Fig. 5C shows the average image of many nanoX structures (averaged over >200 nanobars for each geometry). Therefore, X structures in Fig. 5C do not show a dark center. Fig. 2E reports the averaged cell membrane instead of supported lipid bilayer. The plasma membrane adheres tightly to the ends of nanoX, but does not conform to inner surfaces of nanoXs. This can be seen from the membrane marker mCherry-CAAX signals in Figure 2E, which shows a much higher intensity at the ends of nanoX than that at the ends of nanobars. For Fig. 2E, we were trying to image F-actin, which shows a much clearer signal near the bottom surface. The focal plane is closer to the bottom of nanoX. So, for Fig. 2E, the X structures show a dark center. From the reviewer's comment, we realize that the images can be confusing without proper explanation. We added the focal plane explanation to the main text.
Minor concerns 1. Page 3: "Although it is now established that overexpression of bulky glycocalyx proteins can induce membrane protrusions, the reciprocal relationship ..." This study revealed that the glycoprotein prefers negative membrane curvature. Do these findings provide a new angle to explain the mechanism of glycoproteins inducing protrusions? Could you comment on this mutually beneficial relationship between glycoprotein and protrusions? How are they a pair of mutual causes and effects?
This study supports an entropic-force mechanism that links glycoprotein with membrane protrusions. MUC1 possesses a large ectodomain (head) and a single transmembrane coil (tail). At high packing densities, the large ectodomains entropically repel each other, which exert a force through its transmembrane tail to bend the cell membrane, thus generating membrane protrusions. Conversely, on membrane curvatures that are pre-existing, MUC1 at high densities would prefer protrusions that can effectively reduce the repulsion among ectodomains, as shown in Fig. R13. Figure R13: Entropic repulsion of MUC1 leads to membrane bending.
2. Page 3: "Previous studies by us and others show that when cells are cultured on substrates with vertical nanopillars, the plasma membrane wraps around nanopillars to create local membrane curvatures (Fig. 1B)." Fig. 1B only shows a schematic that the membrane closely wraps the nanostructures. Are there experimental verifications of this basic assumption? The membrane might either closely or loosely wrap the nanostructures, how is this geometry-dependent? This would also provide the rationale of the geometric design (e.g., aspect ratio and pillar height).
For sparse nanopillar arrays such as 200-nm diameter and 2.5 um spacing, the plasma membrane conformally wraps around nanopillars. This is verified by our previous studies using The reviewer is correct that the wrapping is geometry dependent. For larger and densely packed nanopillar arrays, such as 500 nm diameter and 1um spacing, the cell tends to sit on top of nanopillars (Hanson et al. 2012). For complex nanostructures such as nanoX, the membrane does not adhere tightly to the inner surface as previous FIB/SEM study shows that the cell membrane does adhere tightly to concave structures (Santoro et al. 2017). Although negative curvatures will still be induced at the inner faces of nanoXs, the curvature value can not be precisely defined.
We modified the sentence and added the appropriate references: "Previous electron microscopy studies by us and others ( For example, Page 6: "For dense nanopillar arrays, the inter-pillar spacing was small enough to induce the formation of membrane protrusions with negative curvatures in the inter-pillar spaces." How is the inter-pillar spacing optimized here to ensure that the pillars closely imprint membrane protrusions? It is also possible that the membrane could be almost planar on dense pillar tips if the gap space is too small for the membrane to penetrate. BTW, is it possible to provide a zoom-in overlay in Fig. S7 to compare with that in Fig. 2I? The reviewer is correct that "It is also possible that the membrane could be almost planar on dense pillar tips if the gap space is too small for the membrane to penetrate." We have previously used TEM to investigate how cells interface with nanopillar arrays with the dimension used in this study (both sparse and dense arrays). The image shown below is copied from Figure 3 in (Hanson et al. 2012), which shows a cell sitting on top of nanopillars without membrane wrapping. The neuronal cell used in this early study does not have filopodia protrusions. On the other hand, Hela cells that are shown in Fig. 2I and Fig. S9 have extensive filopodial protrusions. MUC1 preferentially locates on these filopodia (Bennett et al. 2001;Hattrup and Gendler 2008). We believe that these protrusions can grow into the inter-pillar space, which is why MUC1 is located at inter-pillar spaces as shown in Fig. 2I. We also provided the zoom-in overlay in Fig. S9, which confirms the same observation ( Figure R15).
To avoid confusion, we revised the text to "Using transmission microscopy, we have previously shown that cells adhere to the bottom surface and wraps around thin and sparse nanopillars (200 nm diameter and 2.5-µm spacing), but stay at the top of dense and large nanopillar arrays (500 nm diameter and 1-µm spacing, same as the one shown in Fig. 2H). For dense nanopillar arrays, the inter-pillar spacing can accommodate or induce the formation of membrane protrusions with negative curvatures. The localization of MUC1 at inter-pillar spaces further supports that MUC1 avoids positive membrane curvatures induced by nanostructures while preferring negative membrane curvatures.".
Copied Figure R6: TEM imaging of cells cultured on nanopillar arrays of different dimensions. The images are copied from (Hanson et al. 2012). Figure R15: Zoom-in overlay of Figure S7. Confocal images of HeLa cells cultured on dense nanopillar arrays with 500-nm diameter and 1-μm spacing. Zoom-in overlay shows that MUC1 is primarily located at the space between nanopillars.
3. Page 5: "MUC1_42TR and MUC1_21TR show spatial avoidance of the nanobar ends." From Fig 1M, it is difficult to see the avoidance on nanobar ends. Would it be helpful if providing the nanobar outline as a guide for the eye? With precise positions indicated by the outline, it might show that the intensity is relatively weaker on the two ends than over the whole bar region (just a suggestion).
The images shown in Fig. 1M are images that are averaged over many nanobars in many cells (detailed statistics provided in the new Table 1). We added the detailed steps to obtain these images at the beginning of the document. The end-to-side ratios are obtained for individual cells. Then the ratios of different proteins are normalized against the mean ratio of the membrane control CAAX. The precise ROI positions that are used to calculate the ratios are shown in Figure R1. The same ROI positions are used to calculate each color channel and each cell. We should have been more precise in our description. We revised the sentence to "Compared to the membrane marker CAAX, MUC1_42TR and MUC1_21TR show reduced signals at the nanobar ends.".


One‐Pot Synthesis of Customized Metal–Phenolic‐Network‐Coated AIE Dots for In Vivo Bioimaging

Abstract The integration of aggregation‐induced emission luminogens (AIEgens) and inorganic constituents to generate multifunctional nanocomposites has attracted much attention because it couples the bright aggregate‐state fluorescence of AIEgens with the diverse imaging modalities of inorganic constituents. Herein, a facile and universal strategy to prepare metal–phenolic‐network (MPN)‐coated AIE dots in a high encapsulation efficiency is reported. Through precise control on the nucleation of AIEgens and deposition of MPNs in tetrahydrofuran/water mixtures, termed as coacervation, core–shell MPN‐coated AIE dots with bright emission are assembled in a one‐pot fashion. The optical properties of MPN‐coated AIE dots can be readily tuned by varying the incorporated AIEgens. Different metal ions, such as Fe3+, Ti4+, Cu2+, Ni2+, can be introduced to the nanoparticles. The MPN‐coated AIE dots with a red‐emissive AIEgen core are successfully used to perform magnetic resonance/fluorescence dual‐modality imaging in a tumor‐bearing mouse model and blood flow visualization in a zebrafish larva. It is believed that the present study provides a tailor‐made nanoplatform to meet the individual needs of in vivo bioimaging.

Introduction
Natural evolution proceeds in a path of balance and holism. By virtue of this principle, natural organic-inorganic (O-I) hybrid materials, such as teeth, bone, shells of marine mussels, reflect the perfect trade-off properties, including durability, elasticity, toughness, hydrophobicity, and other physicochemical properties. [1] To meet the ever-growing demands of modern manufacturing and living, the applications of artificial O-I composites have been boomingly expanded from ancient strawreinforced adobes to advanced composites in the aerospace field because of the development of cross-cutting synthetic methods toward the precise control on the desired characteristics of O-I hybrids. [2] The integration of disparate properties of organic and inorganic constituents via covalent or noncovalent interactions compensates for the inborn defects of a single component and may realize a better performance than parental components due to the unique intercomponent interactions. [3] On the other hand, mature nanotechnology lays a solid foundation on the fabrication of nanocomposites with flexible mastery at morphology, size, composition, and interfacial properties, which extends the use of O-I composites in nanooptics, [4] nanoelectronics, [5] and nanomedicine. [6] O-I hybrid nanoparticles are considered as promising candidates for biomedical imaging and therapy in the era of precision medicine. [7] In a typical O-I hybrid imaging system, the incorporated inorganic constituents, such as noble metal and iron oxide, allow the implementation of computed tomography and magnetic resonance (MR) imaging to provide anatomic information of interest in high resolution, while the concomitant organic fluorophores enable fluorescence imaging with superior sensitivity. [8] Indeed, this synergistic integration enjoys the benefits of fluorescence and other imaging modalities within a single O-I hybrid nanoparticle. [9] Fluorescent O-I hybrid nanoparticles can be fabricated through various methods, including surfactantbased direct wrapping of organic fluorophores and inorganic species, chemical or physical anchoring of organic fluorophores on the surface of inorganic nanoparticles, and other bottom-up synthetic methods. [7] When fluorophores are in close proximity to each other inside the O-I hybrid nanoparticles, their fluorescence is partially or completely quenched due to the detrimental interfluorophore interactions. [10] Such a phenomenon of aggregation-caused quenching (ACQ) is commonly observed in conventional planar fluorophores and can be prevented by minimizing the fluorophore loading content. However, the photostability of the fluorescent O-I hybrid nanoparticles will be significantly sacrificed when the mount of fluorophores is reduced.
Various strategies have been proposed to improve the luminous efficiency of fluorophores in the aggregate state. [11] Among them, aggregation-induced emission (AIE) luminogens elegantly overcome the ACQ problem and its discovery also initiates the thriving study of aggregology. [12] The aggregates of AIE luminogens (AIEgens) show brighter emission than their molecular building blocks to enable them as competitive alternatives to construct fluorescent O-I hybrid nanoaggregates. [13] The combination of AIEgens and inorganic components to generate O-I hybrids for biomedical applications has been explored, but the present synthetic methods to these composites are still unsatisfactory in terms of universal applicability. For example, the chemical conjugation of AIEgens and metal chelates can unify the originally separated properties of individuals to achieve dual-modality imaging. [14] However, it requires convoluted synthesis and is merely workable for simple and stereotyped tetraphenylethylene (TPE) derivatives with a limited emission wavelength. As may easily be imagined, the development of red-emissive or near-infrared-emissive AIEgen-metal conjugates using chemical synthesis will be extremely laborious and time-consuming. Thus, it is highly desired to develop a simple yet efficient approach to prepare fluorescent O-I nanocomposites with regulable modules to fulfill the diverse needs of biological applications.
To take this challenge, we herein report a one-pot synthetic method to fluorescent O-I hybrid nanoparticles with broad applicability. By virtue of a phase separation process, termed as coacervation, nucleation of hydrophobic AIEgens and synergistic depo-sition of 3D cross-linked tannic acid/metal ion complexes on the surface of the formed AIEgen nanoaggregates afford core-shell AIEgen/metal ion nanohybrids in tetrahydrofuran (THF)/water mixtures. In this method, both AIEgens and metal ions are replaceable to provide possibilities to customize fluorescent O-I hybrid nanoparticles for extensive theranostic applications. As a proof of concept, in vivo MR and fluorescence imaging of AIEgen/Fe 3+ nanohybrids are successfully demonstrated.

Synthesis, Characterization, and Photophysical Properties of 2TPEA-AQ
A new red-emissive AIEgen, named as 2TPEA-AQ, was designed and synthesized, as shown in Figure 1a. 2TPEA-AQ was readily obtained through Buchwald-Hartwig amination using Pd 2 (dba) 3 and RuPhos as catalyst and ligand, respectively. This strategy is reported to be highly efficient for the formation of C-N bonds. [15] Indeed, 2TPEA-AQ was generated in a high yield of 82%. The resulting molecule was fully characterized by 1 H and 13 C NMR spectroscopy, and high-resolution mass spectrometry with satisfactory results (Figures S1-S3, Supporting Information). The photophysical properties of 2TPEA-AQ were studied by UV-vis and photoluminescence (PL) spectroscopy. As shown in Figure 1b, 2TPEA-AQ gives an absorption maximum at 455 nm. Electron and hole natural transition orbital analysis in the gas phase suggested that the low-energy absorption band of the molecule was contributed by highest occupied molecular orbital (HOMO) → lowest unoccupied molecular orbital (LUMO) and HOMO-2 → LUMO transitions with coefficients of 0.599 and 0.231, respectively ( Figure S4, Supporting Information). The AIE effect of 2TPEA-AQ was studied in THF/water mixtures with varying water fractions. In the pure THF solution, 2TPEA-AQ barely fluoresced (Figure 1b,c). It indicates that the motorized molecular structure of 2TPEA-AQ facilitates the nonradiative decay in the solution state. Even up to 50% of water was added to the THF solution, no obvious fluorescence was detected. However, when the water fraction reached 60%, the intrinsic emission of 2TPEA-AQ started to recover and reached its maximum intensity at a water fraction of 99% (Figure 1c). Such a photophysical behavior demonstrates the AIE characteristic of 2TPEA-AQ. The PL spectrum of 2TPEA-AQ powders was peaked at 630 nm and the associated PL quantum yield was determined to be 5.9% by an integrating sphere. Additionally, the fluorescence lifetime was calculated to be 2.5 ns ( Figure S5b, Supporting Information). This result suggests that 2TPEA-AQ emits no delayed fluorescence, which is different from the reported anthraquinone-based compounds. [16] Scanning electron microscope (SEM) was then utilized to investigate the morphology of 2TPEA-AQ aggregates formed in different THF/water fractions (Figure 1d). Distorted nanofibers were observed at 80% water fraction. Further increasing the water fraction to 90% led to the formation of rod-like particles. When the THF content was minimized to 1%, the 2TPEA-AQ molecules were conformably clustered into nanoparticles. Thus, the size of 2TPEA-AQ aggregates became smaller at increasing water content. This intriguing aggregation behavior of 2TPEA-AQ inspired us to probe its real molecular conformation and arrangement by single-crystal analysis. Single crystals of 2TPEA-AQ were grown in vials at ambient conditions by slow diffusion of hexane into its chloroform solution. As depicted in Figure 1e and Table S1 (Supporting Information), 2TPEA-AQ crystallizes in the triclinic P−1 space group with an elemental cell containing two molecules. Its crystal packing shows various sorts of intermolecular interactions, including C-H···O and partial ··· interactions in the crystal lattice. On this occasion, the intramolecular motions of 2TPEA-AQ are locked due to the physical constraints, which retards the nonradiative relaxation pathways to allow 2TPEA-AQ to fluoresce intensively. Unfortunately, after standing for some time, these emissive nanoaggregates tended to agglomerate into visible particles, suggestive of the colloidal instability of 2TPEA-AQ nanoaggregates in water. Such a drawback limits their applications in biological environment.

Preparation and Characterization of 2TPEA-AQ@AIE-TFe Dots
Tannic acid (TA) is a biocompatible and water-soluble compound approved by the US Food and Drug Administration. As a typical plant polyphenol, TA can serve as a templating and stabilizing agent in the preparation of mesoporous silica nanoparticles, gold nanoparticles, and silver nanoparticles. [17] Furthermore, the coor-dination of TA and metal ions can yield adhesive metal-phenolic networks (MPNs). [18] The deposition of O-I hybrid MPNs on multifarious nanomaterials can endow them with an excellent colloidal stability and advanced biological applications. [19] Unlike traditional hydrophilic dyes, AIEgen nanoaggregates can provide an idea template for the coating of MPNs due to their hydrophobic nature. Therefore, it deserves to explore the possibility of interfacial adhesion on AIEgen nanoaggregates using MPNs to construct fluorescent O-I hybrid AIE dots. TA/Fe 3+ complexes were used to test our hypothesis. As shown in Figure 2a, a THF solution of 2TPEA-AQ was dropwise added into an aqueous solution of TA/Fe 3+ complexes under rapid stirring, where the three solutes were kept at an identical molar concentration, followed by raising the pH of the resultant mixture using a 3-(N-morpholino)propanesulfonic acid (MOPS) buffer. Surprisingly, scattered colloidal nanoparticles, coined as 2TPEA-AQ@AIE-TFe dots, were observed by SEM (Figure 2b). Dynamic light scattering (DLS) analysis of 2TPEA-AQ@AIE-TFe dots showed an average size of 95 nm with a polydispersity index (PDI) of 0.115 ( Figure 2c and Table S3 (Supporting Information)). As comparison, AIE dots wrapped by 1, 2-Distearoylsn-glycero-3-phosphoethanolamine-Poly(ethylene glycol) (DSPE-PEG) were fabricated as a control with a PDI of 0.233 (Figures S17 and S21a, Supporting Information). Obviously, the new nanofabrication method can provide a respectable PDI for the practical application of 2TPEA-AQ@AIE-TFe dots. The successful preparation of 2TPEA-AQ@AIE-TFe dots motivates us to thoroughly investigate the entire self-assembly procedure. The morphology of TA/Fe 3+ complexes before the addition of 2TPEA-AQ was characterized by transmission electron microscopy (TEM). Figure S6 (Supporting Information) reveals that TA and Fe 3+ in water with a feed mole ratio of 1:1 could form ununiform nanocomplexes with a diameter of ≈10 nm. The addition of 2TPEA-AQ to the abovementioned solution resulted in the formation of core-shell nanoparticles, as evidenced by the TEM images in Figure 2d and Figure S7 (Supporting Information). To validate the core-shell structure, 2TPEA-AQ@AIE-TFe dots residing on a copper grid were subjected to the treatment of THF etching several times, as THF dissolved only AIEgens rather than TA/Fe 3+ complexes. After the removal of AIEgens, hollow structure was observed by TEM in Figure S8 (Supporting Information). It indicates that the core part is exactly the nanoaggregates of 2TPEA-AQ. Element mapping was performed for 2TPEA-AQ@AIE-TFe dots by energy dispersive spectroscopy. As shown in Figure 2e, the blue dots representing iron elements scatter on the peripheral region of the nanoparticles, which suggests that TA/Fe 3+ complexes compose the shell with high contrast. The energy-dispersive X-ray spectrum (EDX) and quantitative results of elemental compositions (C, N, O, and Fe) of 2TPEA-AQ@AIE-TFe dots are shown in Figure S9 and Table S2 (Supporting Information). The atomic content of the doped iron was measured to be 0.26% and the surface zeta potential of the core-shell nanoparticles was determined to be −28.3 mV probably due to the deprotonation of TA molecules on the surface of the nanoparticles (Table S3, Supporting Information). X-ray photoelectron spectroscopy (XPS) provides the composition information of the extreme surface area (1-10 nm) of the investigated materials. As shown in Figure S10a (Supporting Information), both the measured oxygen and iron contents by XPS are significantly higher than the EDX results. Therefore, it testifies the presence of TA and iron in the outer shell of 2TPEA-AQ@AIE-TFe dots. Figure S10b (Supporting Information) reveals a Fe 2p 3/2 peak at 712.6 eV with a split of 2p peak of 13 eV, indicating the existence of Fe 3+ in the TA/Fe 3+ complexes. The O 1s core-level spectrum in Figure S10c (Supporting Information) displays the peaks at 533.7 and 532.3 eV. The peak at 533.7 eV could be assigned to HO-C groups of TA and HO-Fe species, while another peak at 532.3 eV could be attributed to O═C of TA and O-Fe species, respectively. [20]

Coacervation and Universal Applicability
To further decipher the self-assembly mechanism of TA/Fe 3+ complexes and AIEgen aggregates in the THF/water mixture, a concept, termed as coacervation or phase separation, is introduced. [21] Coacervation describes a process in which the precipitation of polymer aggregates (coacervates) on the surface of minute core materials is triggered by external stimuli, including temperature, pH, antisolvent, salt, etc. [22] Likewise, the selfassembly process in our case is a typical example of coacervation. As shown in Figure 3a, the preparation of the nanoparticles begins with the dispersion of AIEgens and TA/Fe 3+ complexes in THF/water mixtures. The nucleation of AIEgens rapidly develops once the THF solution becomes supersaturated upon mixing with water. At this very moment, the sufficient mixing of THF and water also squeezes the previously dissolved TA/Fe 3+ complexes from the solution to form a coacervate phase. As evidenced in Figure S11 (Supporting Information), the desolvation of TA/Fe 3+ complexes caused by the addition of THF resulted in the generation of massive micrometer-sized aggregates. Thus, THF was thought to serve as a coacervation agent or an inducing agent. Subsequently, the deposition of TA/Fe 3+ complexes on AIEgen aggregates occurs owing to the strong -interactions between the phenyl rings of AIEgens and the galloyl groups of TA (Figure 2a). [23] At last, the adjustment of pH to neutral allows the completion of wall hardening to afford TA-/Fe 3+ -complex-coated AIE dots. It is noteworthy that maintaining the nucleation rate of AIEgens and the deposition rate of TA/Fe 3+ complexes in balance is vitally important in the self-assembly procedure, otherwise big aggregates or insoluble precipitates may form.
To certify the universal applicability of the proposed strategy, other AIEgens, such as Hexaphenylsilole (HPS) and TPETPA-FN, were used to fabricate AIE-TFe dots. Both AIEgens could be shaped into colloidal nanoparticles upon self-assembly with TA/Fe 3+ complexes (Figure 3b and Figure S12 (Supporting Information)). 2TPE-NDTA is a reported photothermal agent that harvests and transfers photoenergy as heat, [24] and it was fabricated into AIE-TFe dots using the same method ( Figure S12c, Supporting Information). The obtained nanoparticles can potentially be applied in photothermal therapy and photoacoustic imaging with the given functionalities of TA/Fe 3+ complexes. The PDI of TPETPA-FN@AIE-TFe dots, HPS@AIE-TFe dots, and 2TPE-NDTA@AIE-TFe dots is 0.06, 0.122, and 0.053, respectively. Therefore, the uniformity of AIE dots fabricated using the one-pot method is decent. Besides uncharged AIEgens, TPE-COOH is a negatively charged AIEgen and formed vesicles with TA/Fe 3+ complexes due to the additional coordination between the carboxylic acid group and Fe 3+ ( Figure S13, Supporting Information). However, the present strategy is not applicable to AIEgens with a positive charge, such as TPE-TMX and TPE-EP ( Figure S14, Supporting Information). Macroscopic aggregates were formed when TPE-TMX or TPE-EP was mixed with TA/Fe 3+ complexes. The formation of large aggregates may be ascribed to the low surface zeta potential of AIE-TFe dots resulting from the neutralization effect of the positively charged AIEgens in the inner part, which provokes the agglomeration of AIE-TFe dots.
The stability of 2TPEA-AQ@AIE-TFe dots was investigated in a phosphate buffer solution (PBS) and a commonly used cell culture medium, namely, Dulbecco's Modified Eagle Medium (DMEM), with 10% fetal bovine serum (FBS). As shown in Figure 3c, the size of 2TPEA-AQ@AIE-TFe dots in PBS increases with increasing incubation time, which reveals that AIE-TFe dots tend to agglomerate into microparticles in a concentrated salt solution due to the charge screening effect of salts. [25] However, the nanoparticles exhibit high stability in DMEM. Given that TA can form multiple hydrogen bonds and hydrophobic interactions with proteins, [26] it is inferred that proteins may work as stabilizers since there are plenty of proteins in DMEM with 10% FBS. Thus, the stability testing assay was carried out by incubation of human serum albumin (HSA) and 2TPEA-AQ@AIE-TFe dots in PBS. The result in Figure 3c suggests that the addition of HSA indeed avoids the agglomeration of nanoparticles and reinforces their stability in PBS. This discovery demonstrates the great potential of AIE-TFe dots for bioapplications since proteins always exist in the biological environment.

Tunable Features of 2TPEA-AQ@AIE-TM Dots
The effect of Fe 3+ concentration on the size and encapsulation efficiency (EE) of 2TPEA-AQ@AIE-TFe dots was investigated by DLS and SEM. Both 2TPEA-AQ and TA concentrations were kept constant at 0.2 × 10 −3 m. The EE of 2TPEA-AQ@AIE-TFe dots was calculated using UV-vis spectrophotometry ( Figure S15 and Table S3, Supporting Information). When the concentration of Fe 3+ was varied from 0.2 × 10 −3 to 0.0125 × 10 −3 m, the EE remained at above 80%, indicative of the high efficiency of the wrapping method (Figure 4a). The size of 2TPEA-AQ@AIE-TFe dots initially decreased slightly and then increased with the increased Fe 3+ concentration. The SEM images given in Figure S16 (Supporting Information) demonstrate spherical AIE-TFe dots irrespective of the Fe 3+ concentration. However, in the absence of Fe 3+ , no nanoparticles were formed because TA is too hydrophilic to encapsulate 2TPEA-AQ nanoaggregates. When the Fe 3+ concentration was raised to 0.4 × 10 −3 m, the resulting nanoparticles were rapidly precipitated. Therefore, the successful coacervation of AIE-TFe dots requires a moderate ratio of TA to Fe 3+ to warrant the stability of formed AIE-TFe dots.
The photophysical properties of 2TPEA-AQ@AIE-TFe dots with varying Fe 3+ concentrations were characterized by UV-vis and PL spectroscopy (Figure 4b,c). As shown in Figure 4b, the absorption in the near-infrared (NIR) region is gradually elevated by increasing the Fe 3+ concentration. Meanwhile, the absorption spectrum of TA/Fe 3+ complexes in water displays a peak at www.advancedsciencenews.com www.advancedscience.com 580 nm, which is assigned to the ligand-to-metal charge-transfer band ( Figure S20a, Supporting Information). [27] Thus, the increased absorption of 2TPEA-AQ@AIE-TFe dots is ascribed to the coordination of TA and Fe 3+ . Figure 4c shows that 2TPEA-AQ@AIE-TFe dots become more emissive upon decreasing the Fe 3+ concentration. At a Fe 3+ concentration of 0.025 × 10 −3 or 0.0125 × 10 −3 m, the nanoparticles show stronger emission than the DSPE-PEG-wrapped AIE dots. It manifests that the fluorescence of 2TPEA-AQ@AIE-TFe dots can be regulated via dark resonance energy transfer (DRET) from fluorescent AIEgens to nonfluorescent TA/Fe 3+ complexes. [28] The 2TPEA-AQ@AIE-TFe dots prepared at a Fe 3+ concentration of 0.1 × 10 −3 m exhibited an abnormally low PL owing to their small size (Figure 4a). It enlarges the specific surface area to facilitate the excited-state molecular motion of the AIEgens and the DRET process to quench the emission. [29] The molar ratio of three components in AIE-TFe dots was optimized to be 1:1:1 for the following investigations. The coordination behavior of TA and Fe 3+ was reported to be pH-dependent, [30] and there are three transition states of TA/Fe 3+ complexes at different pH ranges, including mono-, bis-, and tris-complex states (Scheme S1, Supporting Information). Thus, the PL and size change of 2TPEA-AQ@AIE-TFe dots at different pH were investigated (Figure 4d). By a gradual decrease of pH from 8 to 1, both the PL and size kept almost unchanged in the initial stage. Subsequently, an abrupt change point was identified at pH ≈ 3, where a transition from the bis-complex state to the mono-complex state may happen on the surface of the nanoparticles, as proved by the absorption decrease in Figure S18b (Supporting Information). Definitely, the disassembly of TA/Fe 3+ complexes reduces the DRET efficiency to intensify the fluorescence ( Figure S18a, Supporting Information). Meanwhile, the destructed shell part at pH < 3 caused the agglomeration of the nanoparticles ( Figure S18c,d, Supporting Information). On the other hand, the external galloyl groups were protonated at such a low pH to neutralize the surface zeta potential, which also induced the growth of the visible aggregates. The release of Fe 3+ from 2TPEA-AQ@AIE-TFe dots upon dialysis was studied in PBS at pH = 5.0 and pH = 7.4 ( Figure S19, Supporting Information). Results suggested that incubation of the nanoparticles in the acidic environment truly accelerated the release of Fe 3+ and boosted the fluorescence.
To enrich the scope of the incorporated metal ions, other metal ions, including titanium cations (Ti 4+ ), copper cations (Cu 2+ ), and nickel cation (Ni 2+ ), were utilized to prepare nanoparticles namely 2TPEA-AQ@AIE-TTi dots, 2TPEA-AQ@AIE-TCu dots, and 2TPEA-AQ@AIE-TNi dots, respectively. The coordination of TA and Ti 4+ in water gave a yellow solution with an absorption band close to 400 nm ( Figure S20b, Supporting Information), while the aqueous solutions of TA/Cu 2+ and TA/Ni 2+ complexes were colorless observed by naked eyes (Figure S20c,d, Supporting Information). As shown in Figure 4e, three types of metal ions can self-assemble with TA and 2TPEA-AQ to afford stable spherical nanoparticles. The DLS data shown in Figure S21a (Supporting Information) suggest that the size of 2TPEA-AQ@AIE-TTi dots is smaller than those of 2TPEA-AQ@AIE-TCu dots and 2TPEA-AQ@AIE-TNi dots, which is consistent with the gradually increased absorption in the NIR region owing to the scattering effect of the large particles ( Figure S21b, Supporting Information). It is worthy to note that both Fe 3+ and Ti 4+ can be regarded as hard Lewis acids, and they can bind to TA strongly. By contrast, both Cu 2+ and Ni 2+ are moderate Lewis acids and they show relatively small binding constants with TA. [31] Therefore, the fast deposition rate of TA/Ti 4+ complexes due to the rapid 3D crosslinking of TA/Ti 4+ complexes prevents the nucleation of 2TPEA-AQ to some extent to generate small-sized 2TPEA-AQ@AIE-TTi dots. XPS results verify the existence of metal ions in the corresponding nanoparticles ( Figure S21c-f and Table S4, Supporting Information). Figure 4f manifests that 2TPEA-AQ@AIE-TCu dots and 2TPEA-AQ@AIE-TNi dots emit stronger PL than DSPE-PEG-wrapped AIE dots and 2TPEA-AQ@AIE-TTi dots. In short, the size and fluorescence of AIE-TM (M = Fe, Ti, Cu, Ni) dots are modulable. By employing variable MPNs as the shell and replaceable AIEgens as the core, the obtained nanoparticles can be tailor-made in terms of specific requirements for practical applications.

Dual-Modality Imaging of 2TPEA-AQ@AIE-TFe Dots
AIE-TM dots potentially provide a versatile nanoplatform for multimodality imaging because of the integration of advantages of MPNs and AIEgens. As a demonstration, 2TPEA-AQ@AIE-TFe dots were first used as visualizers for in vitro and in vivo fluorescence imaging. A549 lung cancer cells were costained with 2TPEA-AQ@AIE-TFe dots and LysoTracker Green and the results were shown in Figure 5a. After incubation with cells for 5 h, most 2TPEA-AQ@AIE-TFe dots resided in endo-/lysosomes, as reflected by the yellow dots in the merged image. The acidic microenvironment in endo-/lysosomes (pH = 4.5-5) is speculated to trigger the agglomeration of 2TPEA-AQ@AIE-TFe dots after internalization by endocytosis. The agglomerates of the nanoparticles fail to escape from the endo-/lysosomes and are trapped there. In vitro cytotoxicity of 2TPEA-AQ@AIE-TFe dots to fibroblast L929 cells of normal mouse and A549 cells was evaluated by Cell Counting Kit-8. While 2TPEA-AQ@AIE-TFe dots exhibited negligible cytotoxicity to L929 cells, the A549 cells retained a high viability of over 80% in the presence of low concentration of 2TPEA-AQ@AIE-TFe dots (Figure 5b). When the concentration of the nanoparticles was raised to 1.6 × 10 −6 or 2 × 10 −6 m, slight toxicity was observed. The higher uptake and retention of the nanoparticles in cancer cells than normal cells and the innate anticancer activity of TA may lead to the slight cellular toxicity. [32] To demonstrate 2TPEA-AQ@AIE-TFe dots as an effective MR contrast agent, their spin-lattice relaxation time or a time constant known as T 1 , was measured in the aqueous solution as a function of Fe 3+ concentration. The longitudinal relaxivity (r 1 ) of 2TPEA-AQ@AIE-TFe dots was determined to be around 8.62 mm −1 s −1 , which is higher than those of the reported TA-/Fe 3+ -complex-coated nanoparticles (Figure 5c). [33] The high relaxivity and red fluorescence of 2TPEA-AQ@AIE-TFe dots enabled their further application for in vivo dual-modality imaging. 2TPEA-AQ@AIE-TFe dots were intratumorally injected into A549 tumor-bearing mice. Gadodiamide is a marketed gadolinium-based contrast agent, and was also injected into the same animal model as a control. T 1 -weighted MR transverse pseudocolor images of A549 tumor-bearing mice and MR signalto-noise ratio of the tumor region were obtained at different  were then injected intravenously into the A549 tumor model to observe the enhanced permeability and retention effect. [34] As revealed in Figure S22b,c (Supporting Information), the MR signal of the nanoparticles was first intensified slowly and then declined after reaching the maximum. To qualitatively assess the biodistribution of 2TPEA-AQ@AIE-TFe dots after intravenous injection, the main organs, such as heart, liver, spleen, lung, kidney, and tumor of the mice were sectioned and their fluorescence images were acquired ( Figure S23, Supporting Information). Most 2TPEA-AQ@AIE-TFe dots were found to accumulate in the liver and spleen. All animal experiments were performed in accordance with the guidelines of the ethical committee of Shanghai Public Health Clinical Center and the regulations of the National Ministry of Health.

Visualization of Blood Flow in a Zebrafish Larva
The blood vascular network in zebrafish larvae is a circuit model composed of dorsal aorta, segmental vessels, cardinal vein, and posterior cardinal vein (Figure 6). [35] In this model, blood is generally pumped out of the heart and exits the dorsal aorta. At the end of the tail, the blood flow will turn 180°and exit the cardinal vein as well as posterior cardinal vein. Therefore, the dorsal aorta, cardinal vein, and posterior cardinal vein were chosen as regions of interest. Based on the bright fluorescence of 2TPEA-AQ@AIE-TCu dots, they were selected to inject into the heart of a wild-type zebrafish larva (zebrafish strain: AB) at 2 days postfertilization ( Figure S24, Supporting Information). The living zebrafish larva was then imaged under a fluorescence microscope. As shown in Figure 6 and Movie S1 (Supporting Information), the strong red fluorescence enables the clear observation of the dorsal aorta at 0 s. The fluorescence in the dorsal aorta gradually became weaker with time. After 10 s, the blood carrying the nanoparticles started to enter the posterior cardinal vein. At last, all the injected nanoparticles, which traveled throughout the vascular network, converged in the vein to emit strong fluorescence. It is believed that the negative zeta potential of the TA-/Cu 2+ -complex-coated nanoparticles minimizes the interactions with the blood vascular wall and contributes to the prolonged retention time in the bloodstream. As a control experiment shown in Figure S25 (Supporting Information), 2TPEA-AQ nanoaggregates were directly injected into the heart of a zebrafish larva, and no red emission was observed in the blood flow postinjection. All the nanoaggregates were rapidly precipitated and the red emission was only found at the injection point. The result manifests that using TA/Cu 2+ complexes as the coatings of AIEgen nanoaggregates can prevent the biofouling of AIEgen nanoaggregates and improve the colloidal stability of AIE dots when they circulate through the vasculature. Therefore, 2TPEA-AQ@AIE-TCu dots give a striking fluorescence imaging performance of the wholebody vascular system in a dynamic and real-time way.

Conclusion
In this work, we have provided a highly effective strategy to couple AIEgens with MPNs to form core-shell fluorescent O-I nanohybrids. 2TPEA-AQ@AIE-TFe dots were constructed via facile coacervation and displayed excellent colloidal stability in the proteinrich biological environment. Their size and fluorescence could be readily regulated. The AIEgens and metal ions were replaceable to provide access to tailor-made AIE-TM dots. As a proof-ofconcept study, 2TPEA-AQ@AIE-TFe dots were successfully used to perform in vivo dual-modality imaging. Fluorescence-guided visualization of blood flow was performed by 2TPEA-AQ@AIE-TCu in a zebrafish larva. The proposed strategy delicately resolves the outstanding conundrum of the flexible integration of AIEgens and inorganic constituents and greatly promotes the development of AIEgen-incorporated O-I nanocomposites with expected features. As the aggregation of AIEgens occurs in step with the deposition of TA/Fe 3+ complexes, the present two-inone method enables the continuous and scalable production of MPN-coated AIE dots, which is of guiding significance for practical nanofabrication.

Supporting Information
Supporting Information is available from the Wiley Online Library or from the author.